Everolimus, a potent mammalian target of rapamycin (mTOR) inhibitor, has shown anticancer activity against various types of cancer, including renal cell carcinoma (RCC); however, little information is available on the efficacy and safety of the combination of everolimus and radiotherapy. We report a case of radiation-induced esophagitis that might have been exacerbated by the sequential administration of everolimus.

Case Presentation

A 63-year-old Japanese man with RCC complained of back pain, and magnetic resonance imaging revealed vertebral metastases. He received radiotherapy (30 Gy/10 fractions) to the T6–10 vertebrae. Everolimus was administered immediately after the completion of radiotherapy. One week later, he complained of dysphagia, nausea and vomiting. An endoscopic examination of the esophagus showed erosive esophagitis in the middle to lower portions of his thoracic esophagus, corresponding to the irradiation field.

Conclusion

Clinicians should be aware that everolimus might lead to the unexpected exacerbation of radiation toxicities.

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis.

Results

We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.

As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins.

Conclusions

This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

Caffeine is one of the most frequently ingested neuroactive compounds. All known mechanisms of apoptosis induced by caffeine act through cell cycle modulation or p53 induction. It is currently unknown whether caffeine-induced apoptosis is associated with other cell death mechanisms, such as autophagy. Herein we show that caffeine increases both the levels of microtubule-associated protein 1 light chain 3-II and the number of autophagosomes, through the use of western blotting, electron microscopy and immunocytochemistry techniques. Phosphorylated p70 ribosomal protein S6 kinase (Thr389), S6 ribosomal protein (Ser235/236), 4E-BP1 (Thr37/46) and Akt (Ser473) were significantly decreased by caffeine. In contrast, ERK1/2 (Thr202/204) was increased by caffeine, suggesting an inhibition of the Akt/mTOR/p70S6K pathway and activation of the ERK1/2 pathway. Although insulin treatment phosphorylated Akt (Ser473) and led to autophagy suppression, the effect of insulin treatment was completely abolished by caffeine addition. Caffeine-induced autophagy was not completely blocked by inhibition of ERK1/2 by U0126. Caffeine induced reduction of mitochondrial membrane potentials and apoptosis in a dose-dependent manner, which was further attenuated by the inhibition of autophagy with 3-methyladenine or Atg7 siRNA knockdown. Furthermore, there was a reduced number of early apoptotic cells (annexin V positive, propidium iodide negative) among autophagy-deficient mouse embryonic fibroblasts treated with caffeine than in their wild-type counterparts. These results support previous studies on the use of caffeine in the treatment of human tumors and indicate a potential new target in the regulation of apoptosis.

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space.

Author Summary

This work describes a statistical method that identifies chemical compounds binding to a target protein given the sequence of the target or distinguishes proteins to which a small molecule binds given the chemical structure of the molecule. As our method can be utilized for virtual screening that seeks for lead compounds in drug discovery, we showed the usefulness of our method in its application to the comprehensive prediction of ligands binding to human androgen receptors and in vitro experimental verification of its predictions. In contrast to most previous virtual screening studies which predict chemical compounds of interest mainly with 3D structure-based methods and experimentally verify them, we proposed a strategy to effectively feedback experimental results for subsequent predictions and applied the strategy to the second predictions followed by the second experimental verification. This feedback strategy makes full use of statistical learning methods and, in practical terms, gave a ligand candidate of interest that structurally differs from known drugs. We hope that this paper will encourage reevaluation of statistical learning methods in virtual screening and that the utilization of statistical methods with efficient feedback strategies will contribute to the acceleration of drug discovery.